Urban ecosystem quality assessment based on the improved remote sensing ecological index

Area of Science:

• Environmental Science
• Remote Sensing
• Ecology

Background:

• The Remote Sensing Ecological Index (RSEI) is crucial for ecosystem quality assessment.
• Land Surface Temperature (LST) in RSEI presents challenges with complex calculations and spatial resolution mismatches.

Purpose of the Study:

• To introduce an improved Remote Sensing Ecological Index (DRSEI).
• To enhance ecosystem quality assessment by integrating air pollution data (PM2.5) into the index.

Main Methods:

• Replaced the LST component in RSEI with the Difference Index (DI) representing PM2.5 concentration.
• Evaluated DRSEI's performance against RSEI in Chongqing's urban area.
• Analyzed the influence of indicator spatial variability on Principal Component Analysis (PCA) loadings.

Main Results:

• DRSEI demonstrated superior performance compared to RSEI in urban ecosystem quality assessment.
• DRSEI showed a stronger correlation with the Ecological Index (EI), closer standard deviation values, and a lower root mean square error.
• DRSEI is more suitable for highly urbanized areas, while RSEI is better for suburban regions.

Conclusions:

• DRSEI effectively assesses urban ecosystem quality and reflects air pollution impacts.
• The study highlights the importance of indicator spatial distribution in ecological index construction.
• Findings offer new insights for urban ecological monitoring and environmental management.